{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model is to be adopted to fit the dataset\n",
    "\n",
    "- random forest\n",
    "- SVM\n",
    "- GMM\n",
    "- neural network\n",
    "\n",
    "And all the preprocess involved as follows:\n",
    "- MFCC as vector directly(X_type=`MFCC vector`)\n",
    "- MFCC as matrix directly(X_type=`MFCC matrix`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils import *\n",
    "\n",
    "import torch\n",
    "from torch import nn\n",
    "from torch.optim import *\n",
    "from tqdm import tqdm\n",
    "from prettytable import PrettyTable\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import os \n",
    "\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import accuracy_score\n",
    "from deepforest import CascadeForestClassifier\n",
    "import joblib\n",
    "\n",
    "matplotlib.rcParams[\"font.family\"] = \"Times New Roman\"\n",
    "matplotlib.rcParams[\"legend.fontsize\"] = 16\n",
    "matplotlib.rcParams[\"xtick.major.size\"] = 12\n",
    "matplotlib.rcParams[\"ytick.major.size\"] = 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "meta data has been dumped at \u001b[32m./data/meta.json\u001b[0m\n",
      "+-------+------+---------------+---------------+\n",
      "|       | size | positive size | negative size |\n",
      "+-------+------+---------------+---------------+\n",
      "| train | 770  |      472      |      298      |\n",
      "|  test | 194  |      119      |       75      |\n",
      "+-------+------+---------------+---------------+\n"
     ]
    }
   ],
   "source": [
    "get_meta_data(\n",
    "    meta_data_file=\"./data/meta.json\",\n",
    "    train_ratio=0.8\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM\n",
    "\n",
    "use SVM(class `SVC`) to train and evaluate the effect of the process effect."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "dataprocess : each sample is shaped as a 1D vector whose shape is (3600, )\n",
    "\n",
    "dimension is not reduced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1133, 1200), (285, 1200))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x, train_y = load_abnormal(return_numpy=True, train=True, meta_path=\"./data/meta_0db_id_00.json\", p_flag=\"normal\", n_flag=\"abnormal\")\n",
    "test_x, test_y = load_abnormal(return_numpy=True, train=False, meta_path=\"./data/meta_0db_id_00.json\", p_flag=\"normal\", n_flag=\"abnormal\")\n",
    "train_x.shape, test_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ASSVM(C=100)\n",
    "model.fit(train_x, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7228070175438597"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(test_x, test_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "use K-Fold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1140, 3600), (1140,))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kf = KFold(n_splits=5)\n",
    "X = np.vstack([train_x, test_x])\n",
    "y = np.hstack([train_y, test_y])\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "1.0\n",
      "1.0\n",
      "1.0\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "for (train_index, test_index) in kf.split(X, y):\n",
    "    train_x = X[train_index]\n",
    "    train_y = y[train_index]\n",
    "    test_x = X[test_index]\n",
    "    test_y = y[test_index]\n",
    "    model = SVC(C=100)\n",
    "    model.fit(train_x, train_y)\n",
    "    print(model.score(test_x, test_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC model has been saved to \u001b[34m./model/svc.joblib\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "model.save_model(\"./model/svc.joblib\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generalization performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\n",
      "[0]\n",
      "[0]\n"
     ]
    }
   ],
   "source": [
    "test_files = [\"./data/real_test/\" + file for file in os.listdir(\"./data/real_test/\")]\n",
    "for file in test_files:\n",
    "    mfcc = get_mfcc_from_file(file, numc=8)[:150]\n",
    "    x = mfcc.reshape(1, -1)\n",
    "    pre_lab = model.predict(x)\n",
    "    print(type(pre_lab), pre_lab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bagging SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Forest\n",
    "\n",
    "use Decision tree(class `DecisionTreeClassifier`) to train and evaluate the effect of the process effect."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "dataprocess : each sample is shaped as a 1D vector whose shape is (3600, )\n",
    "\n",
    "dimension is not reduced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x, train_y = load_abnormal(return_numpy=True, train=True)\n",
    "test_x, test_y = load_abnormal(return_numpy=True, train=False)\n",
    "train_x.shape, test_x.shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = DecisionTreeClassifier(\n",
    "    criterion=\"entropy\"\n",
    ")\n",
    "\n",
    "model.fit(train_x, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9605263157894737"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(test_x, test_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest\n",
    "\n",
    "use Random Forest(class `RandomForestClassifier`) to train and evaluate the effect of the process effect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((912, 3600), (228, 3600))"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x, train_y = load_abnormal(return_numpy=True, train=True)\n",
    "test_x, test_y = load_abnormal(return_numpy=True, train=False)\n",
    "train_x.shape, test_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ASRandomForest(n_estimators=200, criterion=\"gini\")\n",
    "model.fit(train_x, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7473684210526316"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(test_x, test_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "use k-fold(k=5) to test the performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1140, 3600), (1140,))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kf = KFold(n_splits=5)\n",
    "X = np.vstack([train_x, test_x])\n",
    "y = np.hstack([train_y, test_y])\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "1.0\n",
      "1.0\n",
      "1.0\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "for (train_index, test_index) in kf.split(X, y):\n",
    "    train_x = X[train_index]\n",
    "    train_y = y[train_index]\n",
    "    test_x = X[test_index]\n",
    "    test_y = y[test_index]\n",
    "    model = RandomForestClassifier(n_estimators=200, criterion=\"gini\")\n",
    "    model.fit(train_x, train_y)\n",
    "    print(model.score(test_x, test_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForest model has been saved to \u001b[34m./model/random_forest.joblib\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "model.save_model(\"./model/random_forest.joblib\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generalization performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------------------+-------------+-------------+--------------+---------------------------------------------------------+\n",
      "|         wav file        | sample rate |    shape    | split number |               predict label(2s as a split)              |\n",
      "+-------------------------+-------------+-------------+--------------+---------------------------------------------------------+\n",
      "| ./data/real_test/a1.wav |    32000    | (817835, 2) |      25      |     [1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1]     |\n",
      "| ./data/real_test/a2.wav |    32000    | (542720, 2) |      16      |             [0],[0],[0],[0],[0],[0],[0],[0]             |\n",
      "| ./data/real_test/a3.wav |    32000    | (920918, 2) |      28      | [1],[1],[0],[0],[0],[1],[0],[0],[0],[0],[0],[0],[0],[0] |\n",
      "+-------------------------+-------------+-------------+--------------+---------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "test_files = [\"./data/real_test/\" + file for file in os.listdir(\"./data/real_test/\")]\n",
    "table = PrettyTable(field_names=[\"wav file\", \"sample rate\", \"shape\", \"split number\", \"predict label(2s as a split)\"])\n",
    "for file in test_files:\n",
    "    sample_rate, signal = wav_from_file(file)\n",
    "    row = [file, str(sample_rate), str(signal.shape), str(signal.shape[0] // sample_rate)]\n",
    "    labels = []\n",
    "    for i in range(signal.shape[0] // (2 * sample_rate)):\n",
    "        sub_signal = signal[2 * i * sample_rate : 2 * (i + 1) * sample_rate]\n",
    "        mfcc = get_mfcc_from_array(sub_signal, sample_rate, numc=8)[:150]\n",
    "        label = model.predict(mfcc.reshape(1, -1))\n",
    "        labels.append(str(label))\n",
    "    row.append(\",\".join(labels))\n",
    "    table.add_row(row)\n",
    "\n",
    "print(table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "use `frame` data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x = np.load(\"./data/train/features.npy\")\n",
    "train_y = np.load(\"./data/train/labels.npy\")\n",
    "train_x.shape, train_y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Neural network\n",
    "\n",
    "use nueral network, the data is used rawly.\n",
    "\n",
    "framing is adopted, and each data is shaped like : [50, 1024], where 50 means only 50 frames in each wave file is used to train the model and 50 frames are equal to 1.5 seconds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ARNN(\n",
    "    input_dim=1024,\n",
    "    hidden_dim=256,\n",
    "    layer_dim=2\n",
    ")\n",
    "\n",
    "# TODO : change optimizer and see what happen\n",
    "optimizer = SGD(params=model.parameters(), lr=1e-3)\n",
    "# TODO : add weight to the CrossEntropyLoss and see what happen\n",
    "loss_func = nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "why input_dim is 1024? Because it is the sample number in each frame, which can be calculated as:\n",
    "\n",
    "$$\n",
    "    \\text{input\\_dim}=\\text{sample\\_rate} \\div 1000 \\times \\text{frame\\_length}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 5/5 [03:27<00:00, 41.58s/it]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1faaaaa6308>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_losses = []\n",
    "all_acces = []\n",
    "\n",
    "for epoch in tqdm(range(5)):\n",
    "    # TODO : Does normalise have influence on the performance?\n",
    "    loader = DataLoader(\n",
    "        batch_size=128,\n",
    "        train=True,\n",
    "        normalise=True,\n",
    "        shuffle=True\n",
    "    )\n",
    "    for (bx, by) in loader:\n",
    "        output : torch.Tensor = model(bx)\n",
    "        pre_lab : torch.Tensor  = torch.argmax(output, dim=1)\n",
    "        loss : torch.Tensor = loss_func(output, by)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        acc = accuracy_score(pre_lab.flatten(), by.flatten())\n",
    "        all_losses.append(loss.item())\n",
    "        all_acces.append(acc)\n",
    "\n",
    "plt.figure(figsize=[16, 10])\n",
    "plt.plot(all_losses, color=\"orangered\", label=\"loss\")\n",
    "plt.plot(all_acces, color=\"dodgerblue\", label=\"accuracy\")\n",
    "plt.legend(loc=\"upper right\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average accuracy: 0.73828125\n"
     ]
    }
   ],
   "source": [
    "model.eval()\n",
    "loader = DataLoader(\n",
    "    batch_size=64,\n",
    "    train=False,\n",
    "    normalise=True,\n",
    "    shuffle=True\n",
    ")\n",
    "\n",
    "all_acces = []\n",
    "\n",
    "for (bx, by) in loader:\n",
    "    output : torch.Tensor = model(bx)\n",
    "    pre_lab : torch.Tensor  = torch.argmax(output, dim=1)\n",
    "    loss : torch.Tensor = loss_func(output, by)\n",
    "    acc = accuracy_score(pre_lab.flatten(), by.flatten())\n",
    "    all_acces.append(acc)\n",
    "\n",
    "print(\"average accuracy:\", np.mean(all_acces))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deep forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((912, 3600), (228, 3600))"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x, train_y = load_abnormal(return_numpy=True, train=True)\n",
    "test_x, test_y = load_abnormal(return_numpy=True, train=False)\n",
    "train_x.shape, test_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2021-08-23 06:54:20.800] Start to fit the model:\n",
      "[2021-08-23 06:54:20.800] Fitting cascade layer = 0 \n",
      "[2021-08-23 06:54:25.031] layer = 0  | Val Acc = 100.000 % | Elapsed = 4.232 s\n",
      "[2021-08-23 06:54:25.039] Fitting cascade layer = 1 \n",
      "[2021-08-23 06:54:27.601] layer = 1  | Val Acc = 100.000 % | Elapsed = 2.561 s\n",
      "[2021-08-23 06:54:27.601] Early stopping counter: 1 out of 2\n",
      "[2021-08-23 06:54:27.602] Fitting cascade layer = 2 \n",
      "[2021-08-23 06:54:30.045] layer = 2  | Val Acc = 100.000 % | Elapsed = 2.443 s\n",
      "[2021-08-23 06:54:30.045] Early stopping counter: 2 out of 2\n",
      "[2021-08-23 06:54:30.045] Handling early stopping\n",
      "[2021-08-23 06:54:30.046] The optimal number of layers: 1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<deepforest.cascade.CascadeForestClassifier at 0x20a403fba88>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = CascadeForestClassifier()\n",
    "model.fit(train_x, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2021-08-23 06:55:08.803] Start to evalute the model:\n",
      "[2021-08-23 06:55:08.837] Evaluating cascade layer = 0 \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(model.predict(test_x), test_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "use k-fold(k=5) to test the performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1140, 3600), (1140,))"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kf = KFold(n_splits=5)\n",
    "X = np.vstack([train_x, test_x])\n",
    "y = np.hstack([train_y, test_y])\n",
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2021-08-23 06:57:25.131] Start to fit the model:\n",
      "[2021-08-23 06:57:25.132] Fitting cascade layer = 0 \n",
      "[2021-08-23 06:57:30.085] layer = 0  | Val Acc = 100.000 % | Elapsed = 4.953 s\n",
      "[2021-08-23 06:57:30.094] Fitting cascade layer = 1 \n",
      "[2021-08-23 06:57:32.584] layer = 1  | Val Acc = 100.000 % | Elapsed = 2.490 s\n",
      "[2021-08-23 06:57:32.584] Early stopping counter: 1 out of 2\n",
      "[2021-08-23 06:57:32.585] Fitting cascade layer = 2 \n",
      "[2021-08-23 06:57:35.030] layer = 2  | Val Acc = 100.000 % | Elapsed = 2.445 s\n",
      "[2021-08-23 06:57:35.030] Early stopping counter: 2 out of 2\n",
      "[2021-08-23 06:57:35.030] Handling early stopping\n",
      "[2021-08-23 06:57:35.031] The optimal number of layers: 1\n",
      "[2021-08-23 06:57:35.033] Start to evalute the model:\n",
      "[2021-08-23 06:57:35.070] Evaluating cascade layer = 0 \n",
      "1.0\n",
      "[2021-08-23 06:57:36.563] Start to fit the model:\n",
      "[2021-08-23 06:57:36.563] Fitting cascade layer = 0 \n",
      "[2021-08-23 06:57:41.021] layer = 0  | Val Acc = 100.000 % | Elapsed = 4.458 s\n",
      "[2021-08-23 06:57:41.031] Fitting cascade layer = 1 \n",
      "[2021-08-23 06:57:43.492] layer = 1  | Val Acc = 100.000 % | Elapsed = 2.460 s\n",
      "[2021-08-23 06:57:43.492] Early stopping counter: 1 out of 2\n",
      "[2021-08-23 06:57:43.493] Fitting cascade layer = 2 \n",
      "[2021-08-23 06:57:46.090] layer = 2  | Val Acc = 100.000 % | Elapsed = 2.596 s\n",
      "[2021-08-23 06:57:46.091] Early stopping counter: 2 out of 2\n",
      "[2021-08-23 06:57:46.091] Handling early stopping\n",
      "[2021-08-23 06:57:46.092] The optimal number of layers: 1\n",
      "[2021-08-23 06:57:46.093] Start to evalute the model:\n",
      "[2021-08-23 06:57:46.132] Evaluating cascade layer = 0 \n",
      "1.0\n",
      "[2021-08-23 06:57:47.622] Start to fit the model:\n",
      "[2021-08-23 06:57:47.623] Fitting cascade layer = 0 \n",
      "[2021-08-23 06:57:51.960] layer = 0  | Val Acc = 100.000 % | Elapsed = 4.336 s\n",
      "[2021-08-23 06:57:51.968] Fitting cascade layer = 1 \n",
      "[2021-08-23 06:57:54.306] layer = 1  | Val Acc = 100.000 % | Elapsed = 2.338 s\n",
      "[2021-08-23 06:57:54.306] Early stopping counter: 1 out of 2\n",
      "[2021-08-23 06:57:54.307] Fitting cascade layer = 2 \n",
      "[2021-08-23 06:57:56.763] layer = 2  | Val Acc = 100.000 % | Elapsed = 2.456 s\n",
      "[2021-08-23 06:57:56.763] Early stopping counter: 2 out of 2\n",
      "[2021-08-23 06:57:56.763] Handling early stopping\n",
      "[2021-08-23 06:57:56.764] The optimal number of layers: 1\n",
      "[2021-08-23 06:57:56.766] Start to evalute the model:\n",
      "[2021-08-23 06:57:56.803] Evaluating cascade layer = 0 \n",
      "1.0\n",
      "[2021-08-23 06:57:58.356] Start to fit the model:\n",
      "[2021-08-23 06:57:58.357] Fitting cascade layer = 0 \n",
      "[2021-08-23 06:58:02.885] layer = 0  | Val Acc = 100.000 % | Elapsed = 4.528 s\n",
      "[2021-08-23 06:58:02.893] Fitting cascade layer = 1 \n",
      "[2021-08-23 06:58:05.338] layer = 1  | Val Acc = 100.000 % | Elapsed = 2.445 s\n",
      "[2021-08-23 06:58:05.338] Early stopping counter: 1 out of 2\n",
      "[2021-08-23 06:58:05.339] Fitting cascade layer = 2 \n",
      "[2021-08-23 06:58:07.881] layer = 2  | Val Acc = 100.000 % | Elapsed = 2.542 s\n",
      "[2021-08-23 06:58:07.881] Early stopping counter: 2 out of 2\n",
      "[2021-08-23 06:58:07.881] Handling early stopping\n",
      "[2021-08-23 06:58:07.882] The optimal number of layers: 1\n",
      "[2021-08-23 06:58:07.883] Start to evalute the model:\n",
      "[2021-08-23 06:58:07.919] Evaluating cascade layer = 0 \n",
      "1.0\n",
      "[2021-08-23 06:58:09.412] Start to fit the model:\n",
      "[2021-08-23 06:58:09.412] Fitting cascade layer = 0 \n",
      "[2021-08-23 06:58:13.769] layer = 0  | Val Acc = 100.000 % | Elapsed = 4.357 s\n",
      "[2021-08-23 06:58:13.777] Fitting cascade layer = 1 \n",
      "[2021-08-23 06:58:16.225] layer = 1  | Val Acc = 100.000 % | Elapsed = 2.447 s\n",
      "[2021-08-23 06:58:16.225] Early stopping counter: 1 out of 2\n",
      "[2021-08-23 06:58:16.226] Fitting cascade layer = 2 \n",
      "[2021-08-23 06:58:18.699] layer = 2  | Val Acc = 100.000 % | Elapsed = 2.473 s\n",
      "[2021-08-23 06:58:18.699] Early stopping counter: 2 out of 2\n",
      "[2021-08-23 06:58:18.699] Handling early stopping\n",
      "[2021-08-23 06:58:18.700] The optimal number of layers: 1\n",
      "[2021-08-23 06:58:18.702] Start to evalute the model:\n",
      "[2021-08-23 06:58:18.739] Evaluating cascade layer = 0 \n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "for (train_index, test_index) in kf.split(X, y):\n",
    "    train_x = X[train_index]\n",
    "    train_y = y[train_index]\n",
    "    test_x = X[test_index]\n",
    "    test_y = y[test_index]\n",
    "    model = CascadeForestClassifier()\n",
    "    model.fit(train_x, train_y)\n",
    "    print(accuracy_score(model.predict(test_x), test_y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generalization performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2021-08-23 07:00:51.375] Start to evalute the model:\n",
      "[2021-08-23 07:00:51.383] Evaluating cascade layer = 0 \n",
      "[2021-08-23 07:00:51.448] Start to evalute the model:\n",
      "[2021-08-23 07:00:51.455] Evaluating cascade layer = 0 \n",
      "[2021-08-23 07:00:51.516] Start to evalute the model:\n",
      "[2021-08-23 07:00:51.524] Evaluating cascade layer = 0 \n",
      "[2021-08-23 07:00:51.589] Start to evalute the model:\n",
      "[2021-08-23 07:00:51.597] Evaluating cascade layer = 0 \n",
      "[2021-08-23 07:00:51.662] Start to evalute the model:\n",
      "[2021-08-23 07:00:51.670] Evaluating cascade layer = 0 \n",
      "[2021-08-23 07:00:51.731] Start to evalute the model:\n",
      "[2021-08-23 07:00:51.738] Evaluating cascade layer = 0 \n",
      "[2021-08-23 07:00:51.798] Start to evalute the model:\n",
      "[2021-08-23 07:00:51.805] Evaluating cascade layer = 0 \n",
      "[2021-08-23 07:00:51.862] Start to evalute the model:\n",
      "[2021-08-23 07:00:51.869] Evaluating cascade layer = 0 \n",
      "[2021-08-23 07:00:51.931] Start to evalute the model:\n",
      "[2021-08-23 07:00:51.939] Evaluating cascade layer = 0 \n",
      "[2021-08-23 07:00:52.001] Start to evalute the model:\n",
      "[2021-08-23 07:00:52.009] Evaluating cascade layer = 0 \n",
      "[2021-08-23 07:00:52.071] Start to evalute the model:\n",
      "[2021-08-23 07:00:52.080] Evaluating cascade layer = 0 \n",
      "[2021-08-23 07:00:52.151] Start to evalute the model:\n",
      "[2021-08-23 07:00:52.160] Evaluating cascade layer = 0 \n",
      "[2021-08-23 07:00:52.229] Start to evalute the model:\n",
      "[2021-08-23 07:00:52.237] Evaluating cascade layer = 0 \n",
      "[2021-08-23 07:00:52.307] Start to evalute the model:\n",
      "[2021-08-23 07:00:52.316] Evaluating cascade layer = 0 \n",
      "[2021-08-23 07:00:52.389] Start to evalute the model:\n",
      "[2021-08-23 07:00:52.399] Evaluating cascade layer = 0 \n",
      "[2021-08-23 07:00:52.464] Start to evalute the model:\n",
      "[2021-08-23 07:00:52.472] Evaluating cascade layer = 0 \n",
      "[2021-08-23 07:00:52.535] Start to evalute the model:\n",
      "[2021-08-23 07:00:52.543] Evaluating cascade layer = 0 \n",
      "[2021-08-23 07:00:52.610] Start to evalute the model:\n",
      "[2021-08-23 07:00:52.618] Evaluating cascade layer = 0 \n",
      "[2021-08-23 07:00:52.683] Start to evalute the model:\n",
      "[2021-08-23 07:00:52.690] Evaluating cascade layer = 0 \n",
      "[2021-08-23 07:00:52.754] Start to evalute the model:\n",
      "[2021-08-23 07:00:52.761] Evaluating cascade layer = 0 \n",
      "[2021-08-23 07:00:52.826] Start to evalute the model:\n",
      "[2021-08-23 07:00:52.834] Evaluating cascade layer = 0 \n",
      "[2021-08-23 07:00:52.898] Start to evalute the model:\n",
      "[2021-08-23 07:00:52.906] Evaluating cascade layer = 0 \n",
      "[2021-08-23 07:00:52.969] Start to evalute the model:\n",
      "[2021-08-23 07:00:52.976] Evaluating cascade layer = 0 \n",
      "[2021-08-23 07:00:53.040] Start to evalute the model:\n",
      "[2021-08-23 07:00:53.048] Evaluating cascade layer = 0 \n",
      "[2021-08-23 07:00:53.112] Start to evalute the model:\n",
      "[2021-08-23 07:00:53.119] Evaluating cascade layer = 0 \n",
      "[2021-08-23 07:00:53.184] Start to evalute the model:\n",
      "[2021-08-23 07:00:53.192] Evaluating cascade layer = 0 \n",
      "[2021-08-23 07:00:53.257] Start to evalute the model:\n",
      "[2021-08-23 07:00:53.265] Evaluating cascade layer = 0 \n",
      "[2021-08-23 07:00:53.327] Start to evalute the model:\n",
      "[2021-08-23 07:00:53.335] Evaluating cascade layer = 0 \n",
      "[2021-08-23 07:00:53.401] Start to evalute the model:\n",
      "[2021-08-23 07:00:53.409] Evaluating cascade layer = 0 \n",
      "[2021-08-23 07:00:53.471] Start to evalute the model:\n",
      "[2021-08-23 07:00:53.479] Evaluating cascade layer = 0 \n",
      "[2021-08-23 07:00:53.543] Start to evalute the model:\n",
      "[2021-08-23 07:00:53.550] Evaluating cascade layer = 0 \n",
      "[2021-08-23 07:00:53.615] Start to evalute the model:\n",
      "[2021-08-23 07:00:53.622] Evaluating cascade layer = 0 \n",
      "[2021-08-23 07:00:53.687] Start to evalute the model:\n",
      "[2021-08-23 07:00:53.695] Evaluating cascade layer = 0 \n",
      "[2021-08-23 07:00:53.758] Start to evalute the model:\n",
      "[2021-08-23 07:00:53.765] Evaluating cascade layer = 0 \n",
      "+-------------------------+-------------+-------------+--------------+---------------------------------------------------------+\n",
      "|         wav file        | sample rate |    shape    | split number |               predict label(2s as a split)              |\n",
      "+-------------------------+-------------+-------------+--------------+---------------------------------------------------------+\n",
      "| ./data/real_test/a1.wav |    32000    | (817835, 2) |      25      |     [1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1],[1]     |\n",
      "| ./data/real_test/a2.wav |    32000    | (542720, 2) |      16      |             [0],[0],[0],[0],[0],[0],[0],[0]             |\n",
      "| ./data/real_test/a3.wav |    32000    | (920918, 2) |      28      | [1],[1],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0],[0] |\n",
      "+-------------------------+-------------+-------------+--------------+---------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "test_files = [\"./data/real_test/\" + file for file in os.listdir(\"./data/real_test/\")]\n",
    "table = PrettyTable(field_names=[\"wav file\", \"sample rate\", \"shape\", \"split number\", \"predict label(2s as a split)\"])\n",
    "for file in test_files:\n",
    "    sample_rate, signal = wav_from_file(file)\n",
    "    row = [file, str(sample_rate), str(signal.shape), str(signal.shape[0] // sample_rate)]\n",
    "    labels = []\n",
    "    for i in range(signal.shape[0] // (2 * sample_rate)):\n",
    "        sub_signal = signal[2 * i * sample_rate : 2 * (i + 1) * sample_rate]\n",
    "        mfcc = get_mfcc_from_array(sub_signal, sample_rate, numc=8)[:150]\n",
    "        label = model.predict(mfcc.reshape(1, -1))\n",
    "        labels.append(str(label))\n",
    "    row.append(\",\".join(labels))\n",
    "    table.add_row(row)\n",
    "\n",
    "print(table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x, train_y = load_abnormal(\n",
    "    meta_path=\"./data/meta_id_00.json\"\n",
    ")"
   ]
  }
 ],
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